Controlling placement of virtual machines on physical host machines and placement of physical host machines in cabinets

ABSTRACT

A resource management node includes a processor and a memory coupled to the processor. The memory includes computer readable program code that when executed by the processor causes the processor to perform operations. The operations can include, for each of a plurality of guest virtual machines (VMs), determining operational resources of physical host machines available in a distributed computing system that are needed to provide the guest VM. The operations can further include determining an amount of infrastructure of a distributed computing system used by a defined placement of physical host machines in racks of the distributed computing system.

BACKGROUND

The present disclosure relates to computer systems, and in particular tocontrolling deployment of resources within a distributed computingsystem.

Distributed computing systems, sometimes also referred to as cloudcomputing systems, are used to provide services to electronic deviceswhich may be operated by end users. In a cloud computing system, thephysical host machine architecture is hidden from the end user. Thephysical host machines can include servers, network storage devices,computing devices, network routers, network gateways, wireless/wirednetwork interface devices, etc. However, because services are deployedon a physical host machine architecture which is hidden from end users,it can be managed, upgraded, replaced or otherwise changed by a systemadministrator (operator) without the end users being aware of oraffected by the change.

In existing cloud and other distributed computing systems, the creatorof services or the operator of the cloud system must know in advancewhich applications (or types of applications) will be deployed andestimate the number and types of physical host machines that need to bedeployed in the cloud system to support processing of the applications.The capacity of the distributed computer system can be changed byincreasing or decreasing the number or types of physical host machines.During operation, a load balancer can operate to direct requests fromuser electronic devices to particular ones of the physical host machinesfor processing by associated applications. Although load balancers canprovide better balancing of system utilization, they may notsufficiently improve the efficiency at which physical host machines aredeployed and used, which may have a substantial effect on cost in viewof the potential large number of physical host machines and applicationsthat can be deployed in some distributed computer systems.

SUMMARY

Some embodiments disclosed herein are directed to a resource managementnode having a processor and memory coupled to the processor. The memoryincludes computer readable program code that when executed by theprocessor causes the processor to perform operations. The operationsinclude, for each of a plurality of guest virtual machines (VMs),determining operational resources of physical host machines available ina distributed computing system that are needed to provide the guest VM.The operations further include determining an amount of infrastructureof a distributed computing system used by a defined placement ofphysical host machines in racks of the distributed computing system.

In some further embodiments, a recursive determination is performed foran amount of infrastructure of the distributed computing system used bya plurality of different placements of the physical host machines incabinets of the distributed computing system to identify one of theplurality of different placements of the physical host machines in thecabinets that satisfies a defined rule for how much infrastructure ofthe distributed computing system is used. A further recursivedetermination can be performed for an amount of infrastructure of thedistributed computing system used by a plurality of different placementsof the guest VMs on the physical host machines to identify one of theplurality of different placements of the guest VMs on the physical hostmachines that satisfies the defined rule for how much infrastructure ofthe distributed computing system is used.

The defined rule may relate to how much electrical power capacity of thecabinets is allowed to be used, how much cooling capacity of thecabinets is allowed to be used, how much physical rack storage space ofthe cabinets is allowed to be used, and/or which guest VMs are notallowed to be hosted on physical host machines located in a samecabinet.

Some other embodiments are directed to a method of operating a resourcemanagement node that includes, for each of a plurality of guest VMs,determining operational resources of physical host machines available ina distributed computing system that are needed to provide the guest VM.The method further includes determining an amount of infrastructure of adistributed computing system used by a defined placement of physicalhost machines in racks of the distributed computing system.

Some other embodiments are directed to a computer program product thatincludes a computer readable storage medium having computer readableprogram code embodied in the medium that when executed by a processor ofa computer system causes the computer system to perform operations. Theoperations include, for each of a plurality of guest VMs, determiningoperational resources of physical host machines available in adistributed computing system that are needed to provide the guest VM.The operations further include determining an amount of infrastructureof a distributed computing system used by a defined placement ofphysical host machines in racks of the distributed computing system.

Related methods of operating a resource management node are disclosed.It is noted that aspects described with respect to one embodiment may beincorporated in different embodiments although not specificallydescribed relative thereto. That is, all embodiments and/or features ofany embodiments can be combined in any way and/or combination. Moreover,other resource management nodes, distributed computing systems, methods,and/or computer program products according to embodiments will be orbecome apparent to one with skill in the art upon review of thefollowing drawings and detailed description. It is intended that allsuch additional resource management nodes, distributed computingsystems, methods, and/or computer program products be included withinthis description and protected by the accompanying claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the present disclosure are illustrated by way of example andare not limited by the accompanying drawings. In the drawings:

FIG. 1 is a pictorial illustration of a distributed computer system thatis configured as a data center according to some embodiments;

FIG. 2 is a block diagram of a resource node, used in a distributedcomputing system, that is configured according to some embodiments;

FIG. 3 is a block diagram of a distributed computing system that isconfigured according to some embodiments;

FIG. 4 is a resource management node that is configured according tosome embodiments.

FIGS. 5-8 are flowcharts that illustrate operations for determining anamount of infrastructure of a distributed computing system attributed toproviding guest VMs according to some embodiments;

FIG. 9 illustrates information that may be displayed to inform anoperator of which guest VMs of VM clusters satisfy rules for beingproductive versus unproductive, and to further inform the operator ofthe infrastructure utilization of the productive and unproductive guestVMs and associated clusters;

FIG. 10 is a schematic illustration of a cabinet layout in a data centerfor physical host placement according to some embodiments;

FIG. 11 is a flowchart that illustrates operations for determiningplacement of VMs on physical host machines and placement of physicalhost machines in cabinets of a data center that satisfies a defined ruleaccording to some embodiments;

FIG. 12 illustrates information that may be displayed to inform anoperator of the estimated impact that implementation of a guest VM andphysical host machine placement scenario can have on infrastructureutilization of one or more cabinets of a distributed computer systemaccording to some embodiments;

FIGS. 13 and 14 illustrate a cabinet before implementation of VM andphysical host machine placement scenarios and after implementation ofguest VM and physical host machine placement scenarios, respectively,according to some embodiments; and

FIGS. 15-17 are flowcharts that illustrate other operations fordetermining placement of VMs on physical host machines and placement ofphysical host machines in cabinets of a data center that satisfies adefined rule according to some embodiments.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of embodiments of thepresent disclosure. However, it will be understood by those skilled inthe art that the present invention may be practiced without thesespecific details. In other instances, well-known methods, procedures,components and circuits have not been described in detail so as not toobscure the present invention. It is intended that all embodimentsdisclosed herein can be implemented separately or combined in any wayand/or combination.

FIG. 1 is a block diagram of a distributed computer system that isconfigured as a data center 10 according to some embodiments. The datacenter 10 can include hundreds or thousands of data servers which aremounted to physical racks 20 a-20 n arranged in rows for accessibilityby operators. The data servers consume substantial amounts of electricalpower from a power source 30, occupy significant amount of physical rackstorage space of the data center 10, and require substantial coolingheat transfer by a cooling system 32 to remain within an acceptableoperational temperature range. The electrical power, physical rackstorage space, cooling, and other support systems are examples ofinfrastructure provided by the data center 10 to support operation ofthe data servers. In accordance with some embodiments, the data center10 includes a resource management node, which can also be mounted withinone or more of the physical racks 20 a-20 n or separate therefrom, andcan operate according to various embodiments disclosed herein.

The data servers and associated network communication devices areexample physical host machines of the data center 10. The data serversperform computer operations that provide a plurality of guest virtualmachines (VMs) within a plurality of VM clusters. Each VM cluster caninclude a plurality of guest VMs, and each VM cluster can reside ondifferent data servers or may be distributed across more than one dataserver. For each of the guest VMs, the resource management nodedetermines a productivity metric for the guest VM based on resources ofthe data server that are used by the guest VM. Moreover, for each of theguest VMs, the resource management node determines based on theproductivity metric an infrastructure value that indicates an amount ofinfrastructure of the data center 10 attributed to providing the guestVM.

In some further embodiments, the resource management node repeats theseoperations for each of the VM clusters. The infrastructure value may bedetermined based on electrical power consumption by the physical hostmachine which is attributed to providing the guest VM. Theinfrastructure value may additionally or alternatively be determinedbased on physical rack storage space of the distributed computing systemoccupied by the physical host machine which is attributed to providingthe guest VM.

The resource management node can display reports that indicate which VMclusters are determined to satisfy rules for being productive versusunproductive. The reports may alternatively or additionally indicatewhich VM clusters are consuming excessive electrical power, physicalrack storage space, and/or other resources of the data center 10relative to being productive versus unproductive.

In FIG. 1, a report 40 is displayed, on a display device of a datacenter terminal, that identifies the name 42 of one of the serverdevices and identifies the utilization of defined resources 44. Theresource utilizations displayed can include server CPU utilizationpercentage, server physical memory utilization, server available memory,server number of CPUs, free memory, and/or disk input/output busyutilization. The report may further indicate if any of the resources areoperating near defined limits (e.g., excessive server CPU utilizationpercentage is noted by the blackened circle).

A pictorial illustration of the data center 10 may also be displayed.The resource management node may display graphical alarm indications 34a and 34 b at particular locations of the racks where the data centeroperator may want to remove or replace one of the server devices basedon information provided by the resource management node according to oneor more embodiments disclosed herein.

These and other operations are explained in further detail below afterthe following explanation of an example resource node and distributedcomputing system in which the operations may be performed.

Resource Node of a Distributed Computing System

FIG. 2 is a block diagram of a resource node 100 of a distributedcomputing system that is configured according to some embodiments.Referring to FIG. 2, the resource node 100 includes a physical hostmachine 114 that performs computer operations to provide one or more VMclusters 101. Each of the VM clusters includes a plurality of guest VMs104. Each guest VM 104 runs a guest operating system 130 and a pluralityof applications 106 and 108. The computing needs of users (e.g., humansand/or other virtual/non-virtual machines) drive the functionality ofthe VM cluster 101 and guest VMs 104 thereof. A virtual hypervisor 110can provide an interface between the VM cluster 101 and a host operatingsystem 112 and allows multiple guest operating systems 130 andassociated applications 106 and 108 to run concurrently. The hostoperating system 112 is responsible for the management and coordinationof activities and the sharing of the computer resources of the physicalhost machine 114.

The physical host machine 114 may include, without limitation, networkcontent servers (e.g., Internet website servers, movie/televisionprogramming streaming servers, application program servers), networkstorage devices (e.g., cloud data storage servers), network datarouters, network gateways, communication interfaces, program codeprocessors, data memories, display devices, and/or peripheral devices.The physical host machine 114 may include computer resources such as:processor(s) 116 (e.g., central processing unit, CPU); networkinterface(s) 118; memory device(s) 120; data mass storage device(s) 122(e.g., disk drives, solid state nonvolatile memory, etc.); etc. Theprocessor(s) 116 is configured to execute computer program code from thememory device(s) 120, described below as a computer readable storagemedium, to perform at least some of the operations disclosed herein.

Besides acting as a host for computing applications 106 and 108 that runon the physical host machine 114, the host operating system 112 mayoperate at the highest priority level of the resource node 100,executing instructions associated with the physical host machine 114,and it may have exclusive privileged access to the physical host machine114. The host operating system 112 creates an environment forimplementing the VM cluster 101 which hosts the guest VMs 104. One hostoperating system 112 is capable of implementing multiple independentlyoperating VM clusters 101 simultaneously.

The virtual hypervisor 110 (which may also be known as a virtual machinemonitor or VMM) runs on the host operating system 112 and provides aninterface between the VM clusters 101 and the physical host machine 114through the host operating system 112. The virtual hypervisor 110virtualizes the computer system resources and facilitates the operationof the host guest VMs 104 and associated VM cluster 101. The virtualhypervisor 110 may provide the illusion of operating at a highestpriority level to the guest operating system 130. However, the virtualhypervisor 110 can map the guest operating system's priority level to apriority level lower than a top most priority level. As a result, thevirtual hypervisor 110 can intercept the guest operating system 130operations, and execute instructions that require virtualizationassistance. Alternatively, the virtual hypervisor 110 may indirectlyemulate or directly execute the instructions on behalf of the guestoperating system 130. Software steps permitting indirect interactionbetween the guest operating system 130 and the physical host machine 114can also be performed by the virtual hypervisor 110.

The VMs 104 present a virtualized environment to the guest operatingsystems 130, which in turn provide an operating environment for theapplications 106 and 108, and other software constructs.

Distributed Computing System

FIG. 3 is a block diagram of a distributed computing system 300 that isconfigured according to some embodiments. Referring to FIG. 3, thedistributed computing system 300 receives requests from electronicdevices 170 via one or more networks 160′-160″ for processing. Theelectronic devices 170 may be operated by end-users. The distributedcomputing system 300 includes a plurality of resource nodes 100 and aresource management node 150. The electronic devices 170 may include,but are not limited to, desktop computers, laptop computers, tabletcomputers, wireless mobile terminals (e.g., smart phones), gamingconsoles, networked televisions with on-demand media request capability.The resource nodes 100 may be configured as described herein regardingFIG. 2. For some distributed computing systems 300, the number ofresource nodes 100 can number more than a hundred or thousand and thenumber of electronic devices 170 can number more than a thousand orhundred thousand.

The resource management node 150 may operate to distribute individualrequests that are received from the electronic devices 170 to particularones of the resource nodes 100 selected for processing. The resourcemanagement node 150 may select among the resource nodes 100 and/orapplications hosted on VM clusters 101 of the resource nodes 100 fordistributing individual requests responsive to the present loading ofthe resource nodes 110 and/or the VM clusters 101. The loading may bedetermined based on the amount of processing resources, volatile memoryresources, non-volatile mass storage resources, communication resources,and/or application resources that are utilized to process the requests.The resource management node 150 may, for example, operate to distributethe requests responsive to comparison of the relative loadingcharacteristics of the resource nodes 100. The resource management node150 may attempt to obtain a more balanced loading across the resourcenodes 100 to avoid one of the resource nodes 100 operating at more thana threshold loading above other ones of the resource nodes 100.

Example Resource Management Node

FIG. 4 is a block diagram of a resource management node 150 that isconfigured to perform the operations of one of more of the embodimentsdisclosed herein. The resource management node 150 can include one ormore network interfaces 420, one or more processors 400 (referred to as“processor” for brevity), and one or more memories 410 (referred to as“memory” for brevity) containing program code 412.

The processor 400 may include one or more data processing circuits, suchas a general purpose and/or special purpose processor (e.g.,microprocessor and/or digital signal processor) that may be collocatedor distributed across one or more networks. The processor 400 isconfigured to execute program code 412 in the memory 410, describedbelow as a computer readable medium, to perform some or all of theoperations for one or more of the embodiments disclosed herein.

VM Cluster Utilization of Distributed Computing System Infrastructure

As explained above, a distributed computing system (e.g., data center)can include hundreds or thousands of physical host machines (e.g., dataservers). The physical host machines perform computer operations thatprovide a plurality of guest VMs within a plurality of VM clusters. EachVM cluster can include a plurality of guest VMs.

FIGS. 5-8 illustrates operations that can be performed by a resourcemanagement node 150.

Referring to FIG. 5, for each of the guest VMs of a VM cluster, theresource management node 150 determines (block 500) a productivitymetric for the guest VM based on resources of the physical host machinethat are used by the guest VM. Moreover, for each of the guest VMs, theresource management node 150 determines (block 502), based on theproductivity metric for the guest VM, an infrastructure value thatindicates an amount of infrastructure of the distributed computingsystem 300 attributed to providing the guest VM. The resource managementnode 150 may also display (504) information for viewing by an operatorbased on the infrastructure values. The resource management node canrepeat the operations of blocks 500, 502, and 504 for each of the VMclusters.

The infrastructure value may be determined based on electrical powerconsumption by the physical host machine which is attributed toproviding the guest VM. The infrastructure value may additionally oralternatively be determined based on physical rack storage space of thedistributed computing system occupied by the physical host machine whichis attributed to providing the guest VM. Other infrastructure of thedistributed computing system that can be attributed to being used toprovide the guest VM can include one or more of: cooling systemutilization, processing throughput, memory utilization, nonvolatile massdata storage utilization, communication input/output utilization, andapplication resource utilization (e.g., what types and how manyapplications programs).

The resource management node 150 may determine (block 500) theproductivity metric for a guest VM by, for example, determining theprocessor loading created by the guest VM, determining an amount ofmemory used by or allocated to the guest VM, determining an amount ofnonvolatile mass data storage that is used by or allocated to the guestVM, determining communication input/output bandwidth used by the guestVM, and/or a number of applications that are used by the guest VM.

The resource management node 150 may determine (block 502) theinfrastructure value that indicates an amount of infrastructure of thedistributed computing system attributed to providing the guest VM by,for example, determining an amount of electrical power consumed by thephysical host machine to provide the guest VM, determining physical rackstorage space occupied by the physical host machine to provide the guestVM, determining cooling system resources of a distributed computingsystem that are used by the physical host machine to provide the guestVM, determining communication bandwidth resources of a distributedcomputing system that are used by the physical host machine to providethe guest VM, determining data storage resources of a distributedcomputing system that are used by the physical host machine to providethe guest VM etc.

FIG. 7 illustrates operations that can be performed by the resourcemanagement node 150 to determine an amount of infrastructure of thedistributed computing system 300 attributed to providing a guest VM.Referring to FIG. 7, the resource management node 150 determines (block700) a physical rack storage space of the distributed computing system300 that is occupied by the physical host machine and which isattributed to providing the guest VM, based on the productivity metricfor the guest VM. The electrical power consumption by the physical hostmachine which is attributed to providing a guest VM can be determined(block 702) based on the productivity metric for the guest VM. A coolingmetric (e.g., British thermal units, rack cooling index, etc.) used bythe physical host machine which is attributed to providing a guest VMcan be determined (block 704) based on the productivity metric for theguest VM.

Determining the physical rack storage space, the electrical powerconsumption, and/or the cooling metric can be based on knownrelationships between the physical host machine and definedinfrastructure characteristics that are known or observed to be consumedby the physical host machine operating at different loading. Therelationships may be specified by manufacturers and/or operators,calculated, and/or developed by data collection processes that caninclude monitoring effects that different observed guest VM loadingand/or VM cluster loading have on the infrastructure requirements of adistributed computing system, such as the electrical power consumption,cooling metrics, and/or physical rack storage space use by the physicalhost machines to provide processing and other resources for the guestVMs and/or VM clusters. A Data Center Infrastructure Management™ (DCIM)tool by CA Technologies may be used to provide information used by therelationships to determine the infrastructure attributed to providing aguest VM.

In one embodiment, the infrastructure attributed to operation of aphysical machine is determined. The determined infrastructure is thenproportionally attributed to each of the guest VMs hosted by thephysical machine based on the relative utilization of the physicalmachine's resources by respective ones of the guest VMs. For example,when four guest VMs each use 10% of a physical machine resource and afifth guest VM uses 60% of the physical machine resource, then theinfrastructure used (power, cooling, space, etc.) by the physicalmachine can be attributed 10% to each of the four guest VMs andattributed 60% to the fifth guest VM. The physical machine resourceutilization may be determined based on any defined resources of thephysical machine that can be used by a guest VM including, but notlimited to, processor utilization, memory utilization, network interfaceutilization, disk input/output utilization, and/or a combinationthereof.

When determining the relative utilization, the physical machine resourceused by overhead system services (e.g., host operating system, dataarchiving applications, virus scanning applications, physical devicemanager applications, display applications, etc.) may be included orexcluded from the calculation. Excluding physical machine resources usedby overhead system services may enable more accurately determination ofhow much infrastructure is needed to support the guest VMs (e.g., howmuch user directed work is done by a physical machine and how muchassociated infrastructure is used to support that work),

For example, electrical power consumption and/or cooling loading by aphysical machine may be defined by a manufacturer and/or operator,and/or measured during operation of the physical machine under variousresource loading scenarios (e.g., processor utilization, memoryutilization, network interface utilization, disk input/outpututilization, and/or a combination thereof). The resource loadingscenarios may be defined to exhibit known characteristics of definedtypes of guest VMs.

Referring again to FIG. 3, the distributed computing system 300 mayinclude a resource parameter repository 154 that contains informationthat identifies relationships between different identified types ofphysical host machines and the quantities of infrastructure (e.g.,electrical power, rack space, cooling, communication bandwidth, datastorage, etc.) of a distributed computing system they respectivelyconsume when operating. The relationships may further identify how thequantities of consumed infrastructure vary with different loading of thephysical host machines. The resource parameter repository 154 mayindicate other attributes of the physical host machines, such as theirstart-up time, shut-down time, peak and average electrical powerutilization, communication bandwidth capabilities, processing throughputcapabilities, data storage capabilities, available applicationresources, etc.

The resource management node can display reports that indicate which VMclusters are determined to satisfy rules for being productive versusunproductive. The reports may alternatively or additionally indicatewhich VM clusters are consuming excessive electrical power, physicalrack storage space, and/or other resources of the data center 10relative to being productive versus unproductive. Example reports areshown in FIG. 1 described above and FIGS. 7 and 8 described furtherbelow.

In one embodiment, a time window over which the productivity metric ismeasured may be adjusted/controlled based on how quickly physical hostmachines can be started up (e.g. brought on-line from an idle state orbooted-up from a power-off state to be available to provide VMs toprovide services to electronic devices 170) and/or shut-down. Referringto FIG. 6, the resource management node 150 may determine theproductivity metric by measuring (block 600) what resources of thephysical host machine are used by the guest VM over a threshold durationthat is defined based on observed change in number of VM guests within aVM cluster over an elapsed time. The resource management node 150 mayalternatively or additionally determine (block 602) an elapsed time fromstart-up of the physical host machine to the physical host machine beingready to provide guest VMs. The threshold duration can be defined (block604) based on the change number of VM guest within a VM cluster and/orbased on the elapsed time between start-up and the physical host machinebeing ready to provide guest VMs. The resources of the physical hostmachine that are used by the guest VM can be measured (block 606) overthe threshold duration.

Thus, for example, it may be desirable to avoid presenting informationthat could cause the resource management node and/or a system operator(human) to make guest VM, physical machine, and/or other managementdecisions based on short duration or momentary changes occurring inproductivity metrics for physical host machines that have a slowerstart-up time. So, slower startup times may cause the resourcemanagement node 150 to use corresponding longer elapsed times over whichthe productivity metrics are measured.

Similarly, it may be desirable to avoid presenting information thatcould cause the resource management node and/or a system operator tomake guest VM, physical machine, and/or other management decisions basedon short duration or momentary changes occurring in productivity metricsfor physical host machines having rapidly changing numbers of guest VMsprovided within a VM cluster. For example, the rapid increase in thenumber of guest VMs may be an indication of an anticipated near-termrapid increase in loading of the physical host machine, which may bebeneficial for a system operator to know. So, observation of greaterchanges in the number of guest VMs provided within a VM cluster maycause the resource management node 150 to use corresponding shorterelapsed times over which the productivity metrics are measured.

Determining Infrastructure Used by Unproductive and Productive VMClusters

In some further optional embodiments, the resource management node 150determines for each VM cluster the amount of infrastructure of thedistributed computing system 300 being used by the VM cluster (e.g.,electrical power, rack space, cooling, communication bandwidth, datastorage, etc.), and can display information to a system operator toenable improved infrastructure consumption management and deployment ofVM guests to VM clusters and VM clusters to physical host machines.

In some embodiments, guest VMs of a VM cluster are sorted betweenproductive and unproductive lists. The infrastructure used by each ofthe VM clusters is determined for each of the productive andunproductive lists of associated guest VMs. FIG. 8 illustratesoperations that may be performed by the resource management node 150 todetermine the productivity of guest VMs and VM clusters, and todetermine the infrastructure they are using.

Referring to FIG. 8, a guest VM is analyzed to determine (block 800)whether the applications performed by the guest VM satisfy a defineduser relevance rule for providing at least a threshold level of clientservices to user clients. The user relevance rule may identifyapplications or characteristics of applications that Make theapplications productive or unproductive. For example, applications thatoperate primarily to provide overhead system services for maintaining aphysical host machine and/or operating a VM cluster (e.g., dataarchiving applications, virus scanning applications, physical devicemanager applications, display applications, etc.) without providingservices for a user can be defined by the user relevance rule to beunproductive. In contrast, applications that provide client services tousers (e.g., word processing applications, database applications,voice-recognition applications, gaming applications, financialapplications, etc.) can be defined by the user relevance rule to beproductive. The resource management node 150 may determine applicationrelevance based on application characteristics information provided byan Application Performance Management™ (APM) tool by CA Technologies.

When a guest VM is determined to be productive, a productivity metric isdetermined (block 802) for the guest VM based on resources of thephysical host machine that are used by the guest VM. The productivitymetric may be determined based on processor utilization, memoryutilization, nonvolatile mass data storage utilization, communicationinput/output interface device utilization, and/or application resourceutilization. The resource management node 150 may receive and useutilization information from a Capacity Management (CAPMAN) tool by CATechnologies. The utilization information can be a time series stream ofutilization values that are observed for defined resources, and may havetime stamps associated therewith to allow averaging or other combiningoperations over defined time periods. The productivity metric may bedetermined over a time period that is defined based on the user definedsetting (e.g., defined number of minutes, days, etc.), characteristicsof the physical host machine, and/or rate of change in resourceutilization.

In one embodiment, the time period over which the productivity metric ismeasured may be adjusted/controlled based on how quickly physical hostmachines can be started up (e.g. brought on-line from an idle state orbooted-up from a power-off state to be available to provide VMs toprovide services to electronic devices 170). Slower startup times maycause the resource management node 150 to use corresponding longer timeperiods over which the productivity metric is measured.

In another embodiment, the time period over which the productivitymetric is measured may be adjusted/controlled based on how quickly thenumbers of guest VMs provided within a VM cluster is changing. So,observation of greater changes in the number of guest VMs providedwithin a VM cluster may cause the resource management node 150 to usecorresponding shorter time periods over which the productivity metric ismeasured.

A determination (block 804) is made whether the productivity metric fora guest VM satisfies a threshold value. When the productivity metric fora guest VM satisfies the threshold value (e.g., the guest VM consumesmore than a threshold amount of resources of the physical host machine),the guest VM is added (block 806) to a listing of productive members ofthe guest VMs of a VM cluster. The productive infrastructure usagevalue(s) (e.g., physical rack storage space, electrical powerconsumption, and/or cooling metric) used by the physical host machinewhich is attributed to providing the guest VM is determined (block 808),For example, each different type of infrastructure parameters can have aseparate productive infrastructure usage value (e.g., physical rackstorage space usage value, power consumption usage value (e.g., averagepower (kW) or total energy (kWh), and/or cooling metric usage value(British thermal unit, etc.)). The resource management node 150 maydetermine the productive infrastructure usage value(s) for a guest VMbased on infrastructure usage information provided by a Data CenterInfrastructure Management™ (DCIM) tool by CA Technologies. Theproductive infrastructure usage value(s) is combined (block 810) withother productive infrastructure values determined for the VM cluster(which includes the guest VM) over a defined time window (e.g., averagedover the defined time window).

In contrast, when a guest VM is determined (block 800) to beunproductive or when a productivity metric for a guest VM is determined(block 804) to not satisfy the threshold value (e.g., the guest VMconsumes less than a threshold amount of resources of the physical hostmachine), the guest VM is added (block 812) to a listing of unproductivemembers of the guest VMs of a VM cluster. The unproductiveinfrastructure usage value(s) (e.g., physical rack storage space,electrical power consumption, and/or cooling metric) used by thephysical host machine which is attributed to providing the guest VM isdetermined (block 814). The resource management node 150 may determinethe unproductive infrastructure usage value(s) for a guest VM based oninformation provided by the DCIM tool. The unproductive infrastructureusage value(s) is combined (block 810) with other unproductiveinfrastructure values determined for the VM cluster (which includes theguest VM) over a defined time window (e.g., averaged over a defined timewindow).

The time window may be adjusted/controlled based on an operator definedsetting, how quickly physical host machines can be started up orshut-down, and/or based on how quickly the numbers of guest VMs providedwithin a VM cluster is changing.

The operations of blocks 800-816 can be repeated for each guest VM ineach of the VM clusters. The productive infrastructure usage values andthe unproductive infrastructure usage values can be displayed (block820) for each of the clusters.

Determining and Displaying Infrastructure Used by Unproductive andProductive VM Clusters

FIG. 9 illustrates information that may be displayed to inform anoperator regarding which VM clusters satisfy rules for being productiveversus unproductive, and to further inform the operator of theinfrastructure utilization of the VM clusters. FIG. 9 illustrates sevenVM clusters named VM Cluster 1, VM Cluster 2, VM Cluster 3, VM Cluster4, VM Cluster 5, VM Cluster 6, and VM Cluster 7 which are referredherein with underlining between the phrases “VM Cluster” and therespective cluster number 1 through 7. Referring to FIG. 9, graphs 906a-906 g are generated that illustrate the average resource utilizationby seven different VM clusters 902 a-902 g over a past week, The averageresource utilization may correspond to the average processorutilization, memory utilization, nonvolatile mass data storageutilization, communication input/output interface device utilization,and/or application resource utilization attributed to providing therespective VM clusters 902 a-902 g over the past week.

Other information that may be displayed can include, alert notifications904 a-904 g which notify an operator when one or more of the VM clusters902 a-902 g is using too much or too little infrastructure. In FIG. 9,the alert notifications 904 a-904 g indicate whether a particular VMcluster is using excessive electrical circuit breaker capacity. Forexample, VM Cluster_4 902 d has a highlighted alert notification 904 dwith a sub-text notification indicating that it is drawing at least 60percent of the maximum electrical power that can be supplied within thecapacity rating of the associated circuit breaker resource of thedistributed computing system 300. Another VM Cluster_6 902 f has ahighlighted alert notification 904 f with a sub-text notificationindicating that it is drawing at least 80 percent of the maximumelectrical power that can be supplied within the capacity rating of theassociated circuit breaker resource of the distributed computing system300. These notifications can inform a system operator that VM Cluster_4902 d and VM Cluster_6 902 f are drawing excessive electrical power, andcan provide such notification before the circuit breaker switches becomeoverloaded. The system operator can thereby take corrective action byshifting guest VMs from those VM clusters to other VM clusters.Moreover, the system operator may determine that VM Cluster_4 902 d andVM Cluster_6 902 f have an operational fault associated with theirphysical host machines because of the excessive electrical power beingconsumed relative to the low average resource utilization indicated intheir respective graphs 906 d and 906 f.The system operator maytherefore initiate procedures to shut-down and replace those physicalhost machines.

The resource management node 150 may respond to detecting the alarmconditions with VM Cluster_4 902 d and VM Cluster_6 902 f by displayingindicia 908 and 910 which can be selected by a system operator toinitiate actions to further analyze and/or remedy the alarm conditionswith VM Cluster_4 902 d and VM Cluster_6 902 f.

Other information that can be displayed can include a graph 920 showingthe relative amount of infrastructure of the distributed computingsystem 300 that is being used by unproductive guest VMs and productiveguest VMs of each of the VM clusters 902 a-902 g. In the example of FIG.9, VM Cluster_1 has 1 productive guest VM and 2 unproductive guest VMs,and consumes a total of about 3 kWatts of electrical power. VM Cluster_2has 5 productive guest VMs and 5 unproductive guest VMs, and consumes atotal of about 10 kWatts of electrical power. VM Cluster_3 has 5productive guest VMs and 6 unproductive guest VMs, and consumes a totalof about 12 kWatts of electrical power. VM Cluster_4 has 4 productiveguest VMs and 12 unproductive guest VMs, and consumes a total of about15.5 kWatts of electrical power. VM Cluster_5 has 2 productive guest VMsand 7 unproductive guest VMs, and consumes a total of about 9 kWatts ofelectrical power. VM Cluster_6 has 11 productive guest VMs and 23unproductive guest VMs, and consumes a total of about 34 kWatts ofelectrical power. VM Cluster_7 has 2 productive guest VMs and 8unproductive guest VMs, and consumes a total of about 10 kWatts ofelectrical power.

A system operator and/or the resource management node may determine fromthe information displayed by the graph 920 that VM Cluster 6 isconsuming substantially more electrical power to provide unproductiveguest VMs relative to what it consumes to provide productive guest VMs.The system operator and/or the resource management node may thereforeperform operations to shift the 11 productive guest VMs from VM cluster6 to other VM clusters, such as VM clusters 4, 5 and 8 which also aredetermined to be consuming substantially more power for purposes ofproviding unproductive guest VMs then for productive guest VMs.

Alternatively or additionally, the system operator may performoperations to terminate operation or reeschedule operation for a latertime for at least some of the unproductive guest VMs on one or more ofthe VM clusters 4-7 which are consuming disproportionate amounts ofelectrical power relative to their usefulness with supporting operationof the productive guest VMs in their respective VM clusters. Thus forexample, guest VMs providing data archiving applications and/or virusscanning that consume substantial electrical power because of theirassociated disk bandwidth, disk input/output operations and processingoperations can be terminated or reschedule for later operation toprovide a more efficient balance of power consumption between theproductive and unproductive guest VMs for those VM clusters.

The excessive power consumption of the nonproductive guest VMs of one ormore of VM clusters 4-7 may indicate the VM cluster is havingoperational problems and should be restarted and/or that the associatedphysical host machine 114 providing the VM cluster is having operationalproblems and should be restarted, shut-down, or replaced. A systemoperation and/or the resource management node may thereby be informed ofproblems before they result in software and/or hardware failure.

Cabinet Layout in Data Center for Physical Host Machine Placement

FIG. 10 is a schematic illustration of a cabinet layout in a data centerfor physical host placement according to some embodiments. The datacenter includes cabinets (also commonly referred to as “racks”) A1-12,B1-12, C1-20, D1-24, and E1-24 arranged in rows and columns foraccessibility by operators. Each cabinet contains physical storagespaces (e.g., mounting spaces on rack(s), use/unit spaces “U-spaces”,etc.) where physical host machines can be installed to host guest VMs.Facilities control units (FCU) have electrical power sources (e.g.,power conditioning and backup power sources), cooling devices, and otherequipment that supports operation of physical host machines in thecabinets. Each cabinet can include a local electrical power supplydevice(s) and a cooling device(s) that cools physical host machinesmounted in the cabinet. In accordance with some embodiments, the datacenter includes a resource management node that may reside within one ormore of the cabinets or separate therefrom (e.g., within an operatorwork station or outside the data center), and is configured to operateaccording to one or more embodiments disclosed herein. A plurality ofoperator work stations 1000 are illustrated having computer processingand display devices for use by operators to monitor operation of thedata center and control operation of the resource management nodeaccording to one or more embodiments disclosed herein.

Generating Placement Scenarios for Placing Guest VMs on Physical HostMachines and Placing Physical Host Machines in Cabinets Based onInfrastructure Utilization

FIG. 11 is a flowchart that illustrates operations by a resourcemanagement node for determining placement of guest VMs on physical hostmachines and placement of physical host machines in cabinets of a datacenter that satisfies one or more defined rules, according to someembodiments.

Referring to FIG. 11, the resource management node obtains (block 1100)a list of guest VMs that are available for placement onto physical hostmachines. The guest VMs can include guest VMs that are already beinghosted on physical host machines located in some of the cabinets, andwhich are to be analyzed for relocation to other physical host machinesthat are already installed in some of the cabinets or which can beinstalled in the cabinets. The guest VMs may alternatively oradditionally include guest VMs that are not yet hosted on physical hostmachines located in any of the cabinets, and which are to be analyzedfor installation onto physical host machines residing in some of thecabinets or which can be installed in the cabinets. The list of guestVMs may be obtained from a repository of guest VM information.

The resource management node can access (block 1104) a repository ofavailable resources that can identify physical host machines, attributesof the physical host machines, available cabinets, and attributes of thecabinets.

The attributes of the physical host machines may include electricalpower consumption, cooling consumption, physical rack storage space,weight, processing capacity, memory capacity, nonvolatile mass datastorage capacity, communication input/output capacity, availableapplication resource (e.g., what types and how many applicationsprograms), and other resources that can be used/consumed to host theguest VMs. The attributes may be defined by an operator or manufacturer,and/or may be determined based on measurements during operation of thephysical host machines. For physical host machines that are alreadyinstalled in the cabinets, the attributes may indicate the remainingresources that are presently available for hosting guest VMs. Incontrast, for physical host machines that are not yet installed in thecabinets, the attributes may indicate manufacturer/operator definedresources or earlier measured attributes of resources that are availablefor hosting guest VMs. Some attributes of the physical host machines maybe determined based on information provided by a Data CenterInfrastructure Management™ (DCIM) tool by CA Technologies.

The attributes of the cabinets may include total capacity for supplyingelectrical power (e.g., circuit breaker capacity) and/or remainingavailable capacity for supplying electrical power in view of presentlyinstalled physical host machines (e.g., identify particular circuitbreakers having identified remaining capacity), total cooling capacityand/or remaining available cooling capacity for providing cooling tophysical host machines, total weight capacity and/or remaining availableweight capacity for supporting physical host machines on racks, totalphysical storage spaces (e.g., defined unit-spaces “U-spaces”) and/orremaining physical storage spaces for installation of physical hostmachines, configuration and characteristics of physical storage spaces,and other capacities of the cabinets usable by physical host machines.Some attributes of the cabinets may be determined based on informationprovided by the DCIM tool by CA Technologies.

The resource management node uses the attributes of the physical hostmachines and the attributes of the cabinets to generate (block 1102) aVM resource utilization data set that identifies estimates of physicalhost machine resource utilization that is needed to operate each of theguest VMs and/or clusters of VMs. The data set can include, for example,electrical power, cooling, physical rack storage space, weight,processing, memory, nonvolatile mass data storage, communicationinput/output, application resource (e.g., what types and how manyapplications programs), and other resources that each of the guest VMsor clusters of guest VMs are estimated to utilize (e.g., consume,occupy, etc) when operating and/or available for operation. Theattributes may further include estimated costs for installing andsetting-up physical host machines (e.g., acquisition costs and/or humanoperator time costs), estimated time delay until physical host machinescan be installed (e.g., based on available work schedule of humanoperator), estimated time delay for physical host machines to bestarted-up to become operational after installation, and/or estimatedoperational costs of physical host machines. The resource managementnode may obtain some of the information used to generate the data setfrom the DCIM tool and/or a Capacity Management™ (CAPMAN) tool by CATechnologies.

Estimation of the physical host machine resource utilization needed tooperate each of the guest VMs and/or clusters of VMs can be based onknown relationships between the guest VMs and/or clusters of VMs andknown and/or observed resources of the physical host machines that areconsumed for their host. For example, video encoding applications canhave greater defined resource utilization than word processingapplications because of their defined/observed higher processor workloadand data storage device input/output bandwidth requirements. Therelationships may be developed by data collection processes that caninclude monitoring effects that different observed guest VM loadingand/or VM cluster loading has on the resources and infrastructurerequirements of the physical host machines, such as the electrical powerconsumption, cooling, and/or physical rack storage space use by thephysical host machines to provide processing and other resources for theguest VMs and/or VM clusters. The guest VMs may be characterized basedon the particular application programs and/or operational functionalitythey are performing or will be performing.

The resource management node can access (block 1108) a repository ofrules defining constraints on the infrastructure of the distributedcomputing system (e.g., resources of the cabinets and physical hostmachines) that can be used to host the guest VMs. The rules may include,but are not limited to, defining one or more of the following:

1) allowed electrical power capacity of the cabinets to be consumed(e.g., defined margin below the maximum electrical power capacity of thecabinet or rack therein);

2) allowed cooling capacity of the cabinets to be consumed (e.g.,defined margin below the maximum cooling capability of the cabinet orrack therein);

3) allowed occupation of physical storage locations in racks of cabinets(e.g., defined margin below the available storage locations);

4) identification of particular guest VMs that are not allowed to behosted on the same physical host machine, located on the same cabinetrack, located in the same cabinet, and/or located in the same datacenter facility (e.g., to provide greater fault tolerance in case offailure of cabinet resources, to provide increased security isolationbetween datasets used by VMs, etc);

5) weight capacity of the cabinets allowed to be used (e.g., definedmargin below the maximum weight support capacity of cabinets);

6) identification of cabinets and/or storage space locations allowed tobe populated with physical host machines;

7) minimum/maximum allowable contiguous physical storage locationsallowed to be used by physical host machines; and

8) other.

The resource management node selects (block 1106) one or more of therules based on operator input, an algorithm, and/or a defined selectionrule. The rule(s) may be selected to, for example, minimize powerconsumption, minimize storage space utilization in defined cabinet racklocations or more generally anywhere in one of more defined cabinets(e.g., minimize used U-space, minimize contiguous U-space to spread outheat dissipation within a cabinet/rack), minimize cooling consumption,minimize hardware cost, minimize software application cost, minimizeoperating and/or maintenance costs, minimize use of defined types ofphysical host machines, minimize use of defined type/class of cabinetrack, increase reliability (e.g., high availability policy(ies)), and/orincrease operational performance (e.g., processing bandwidth andresponsiveness) of guest VMs.

The resource management node generates (block 1110) a placement scenariofor placing guest VMs on the physical host machines and placement of thephysical host machines in the cabinets of the data center or otherdistributed computing system. The placement scenario may includeinstalling defined physical host machines (e.g., which are presentlylocated in any of the cabinets) and/or relocating defined physical hostmachines (e.g., which are presently located in any of the cabinets) todefined locations in the cabinets, and installing new guest VMs and/orrelocating defined guest VMs to defined ones of the physical hostmachines. The placement scenario may additionally include reconfiguringexisting physical host machines to provide different resources forhosting some of the guest VMs.

How much infrastructure of the data center or other distributedcomputing system is used by the placement scenario is determined (block1112). The determination (block 1112) may include repeating some of theestimation processes described above (block 1102) but now directed tothe placement scenario for how guest VMs are proposed to be mapped tophysical host machines and how physical host machines are proposed tomapped to cabinets.

A decision (block 1114) is made whether the placement scenario forplacing guest VMs on the physical host machines and placement of thephysical host machines in the cabinets of the data center or otherdistributed computing system satisfies the selected rule(s). If theselected rule(s) was not satisfied, the operations of blocks 1110-1114are recursively repeated to generate (block 1110) another placementscenario, determine (block 1112) an amount of infrastructure of the datacenter or other distributed computing system used by the placementscenario, and determine whether the selected rule(s) is satisfied oranother rule for terminating the recursive process is satisfied.

When the selected rule is satisfied or the recursive process isotherwise terminated, the placement scenario is communicated (block1116) to a work order generation tool to generate a work order thatlists physical host machines to be installed or relocated to, and/ordecommissioned (e.g., removed) from, defined locations in the cabinetsand lists guest VMs to be installed or relocated to, and/ordecommissioned (e.g., removed) from, defined physical host machines. Thework order may be physically carried out by a human operator and/or maybe at least partially carried out by automated computer processes toinstall and/or relocate guest VMs and/or to decommission physical hostmachines (e.g., shut-down, trigger lower-power idle state, etc.).

After completion of the work order, a repository of system information(e.g., the resource parameter repository 154) may be automaticallyupdated (e.g., by the automated computer processes) and/or manuallyupdated by a human operator with information that identifies the newsystem configuration. The information may, for example, identify theguest VMs, identify which physical host machines are hosting the guestVMs, and/or identify which physical host machines reside in which of thecabinets and/or racks,

Displaying Estimated Effect of Placement Scenario on Infrastructure Use

FIG. 12 illustrates an informational window 1200 that may be displayedto inform an operator regarding the estimated impact that implementationof a guest VM and physical host machine placement scenario can have oninfrastructure utilization of one or more cabinets of a distributedcomputer system over time, according to some embodiments. In FIG. 12, apreferred (targeted) utilization level for a cluster of defined VMs (“VMCluster 4”) is 55 percent (illustrated as line 1202). With aconservative placement model (e.g., selected rules that seek to minimizechanges to the cabinets) for mapping guest VMs to physical host machinesand mapping physical host machines to storage spaces in cabinets, theresource management node projects (estimates) that the infrastructureutilization by the VM cluster will follow the utilization graph valuesof line 1204 over time. The utilization may refer to electrical powerconsumption, cooling consumption, remaining power capacity, remainingcooling capacity, other infrastructure available from the cabinets, or acombination thereof. Thus for example, the resource management node maycombine estimates of power, cooling, capacity, etc (e.g., according to adefined weighting scheme for the different resource attributes) toobtain a value that is graphed.

Moreover, the resource management node estimates that implementation ofthe conservative placement model will result in infrastructure savingsin the distributed computing system of 1.4 kW of electrical power,savings of 3 physical rack storage spaces (U-space), 34 percent increasein circuit breaker capacity, and $1226 savings per year in energy costs.

A user may select a user selectable indicia 1212 to trigger theconservative placement model operations to generate a placement scenarioand determine the utilization and infrastructure savings that may beobtained by performing the conservative placement model. A user mayalternatively select another user selectable indicia 1214 to triggerexecution of an aggressive placement model (e.g., selected rules thatseek to maximize infrastructure savings with less regard to minimizingchanges to the cabinets), which can generate a placement scenario thatperforms greater changes to the cabinets including placement of newguest VMs on physical host machines already installed in the cabinets,relocation of other guest VMs to other physical host machines installedin the cabinets, and installation of new physical host machines andinstallation of guest VMs thereon.

A user may select another user selectable indicia 1210 to define otherrules (e.g., block 1106 in FIG. 11) or select among existing rules(e.g., block 1108 in FIG. 11) to guide generation of further placementscenarios with associated display of projected infrastructureutilization and estimation of savings.

Example Implementation of a Generated Placement Scenario

As explained above, when an acceptable placement scenario is identified,the placement scenario can be communicated (block 1116 in FIG. 11) to awork order generation tool to generate a work order that lists physicalhost machines to be installed or relocated to defined locations in thecabinets and lists guest VMs to be installed or relocated to definedphysical host machines. The work order may be physically carried out bya human operator and/or may be at least partially carried out byautomated computer processes to install and/or relocate guest VMs and/orto shut-down physical host machines.

FIG. 13 illustrates a cabinet labeled “Server ID1” before implementationof a guest VM and physical host machine placement scenario. In contrast,FIG. 14 illustrates the cabinet of FIG. 14 after implementation of theguest VM and physical host machine placement scenario. The work orderhas identified that two particular physical host machines are to beadded to identified storage locations in the cabinet. In the presentexample placement scenario, two “RAID-array” storage devices have beenidentified for being added to a defined slot associated with IPaddresses 10.0.1.9 and 10.0.1.10, and a “filestore” storage device hasbeen identified for being added to another defined slot associated withIP address 10.0.1.15.

The resource management node measured or otherwise determined (e.g.,based on attributes defined in the repository 1104) that the servercabinet has been supplying 8 amps of current to the physical hostmachines, supporting 520 pounds of physical host machines, and providing6000 BTU/hr of heat dissipation to the physical host machines beforeimplementation of the guest VM and physical host machine placementscenario. The resource management node has also estimated thatimplementation of the guest VM and physical host machine placementscenario would cause the server cabinet to supply 16 amps of current tothe physical host machines, support 670 pounds of physical hostmachines, and provide 10000 BTU/hr of heat dissipation to the physicalhost machines.

Accordingly, the example placement scenario of FIGS. 13 and 14 increasesthe infrastructure utilization of the Server ID1 cabinet. Althoughimplementation of the placement scenario may result in greaterinfrastructure utilization in the illustrated cabinet, savings may beobtained in other cabinets by, for example, allowing relocation of guestVMs from physical host machines in the other cabinets to thereconfigured Server ID1 cabinet and associated decommissioning of thosephysical host machines in the other cabinets. The resource managementnode may estimate and display the infrastructure savings in each of theother affected cabinets and/or a combined infrastructure savings. Othersavings may be obtained by minimizing the number of physical hostmachines or optimizing the selected types of physical host machinesadded to the Server ID1 cabinet to support a list of new guest VMs to beinstalled therein.

Further Operations by a Resource Management Node

FIGS. 15-17 are flowcharts that illustrate other operations fordetermining placement of guest VMs on physical host machines andplacement of physical host machines in cabinets of a data center thatsatisfies a defined rule according to some embodiments. As explainedabove regarding FIG. 4, the resource management node can include aprocessor and a memory coupled to the processor. The memory includescomputer readable program code that when executed by the processorcauses the processor to perform operations.

Referring to FIG. 15, the operations can include, for each of aplurality of guest VMs, determining (block 1500) operational resourcesof physical host machines available in a distributed computing systemthat are needed to provide the guest VM. The operations further includedetermining (block 1502) an amount of infrastructure of a distributedcomputing system used by a defined placement of physical host machinesin racks of the distributed computing system.

Referring to FIG. 16, the operations can include recursively determining(block 1600) an amount of infrastructure of the distributed computingsystem used by a plurality of different placements of the physical hostmachines in cabinets of the distributed computing system to identify oneof the plurality of different placements of the physical host machinesin the cabinets that satisfies a defined rule for how muchinfrastructure of the distributed computing system is used. Theoperations can further include recursively determining (block 1602) howmuch infrastructure of the distributed computing system is used by aplurality of different placements of the guest VMs on the physical hostmachines to identify one of the plurality of different placements thatsatisfies the defined rule for how much infrastructure of thedistributed computing system is used.

The operations for determining an amount of infrastructure used bydifferent placements can include looking at how much power, cooling,and/or storage space would be used. For example, referring to FIG. 17,the operations can include recursively determining (block 1700) anamount of electrical power capacity of the cabinets, an amount ofcooling capacity of the cabinets, and/or an amount of physical rackstorage space of the cabinets used by the plurality of differentplacements of the physical host machines in the cabinets of thedistributed computing system to identify one of the plurality ofdifferent placements that satisfies a defined rule for how muchelectrical power capacity of the cabinets, how much cooling capacity ofthe cabinets, and/or how much physical rack storage space of thecabinets is allowed to be used.

Further Definitions and Embodiments:

In the above-description of various embodiments of the presentdisclosure, aspects of the present disclosure may be illustrated anddescribed herein in any of a number of patentable classes or contextsincluding any new and useful process, machine, manufacture, orcomposition of matter, or any new and useful improvement thereof.Accordingly, aspects of the present disclosure may be implemented inentirely hardware, entirely software (including firmware, residentsoftware, micro-code, etc.) or combining software and hardwareimplementation that may all generally be referred to herein as a“circuit,” “module,” “component,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productcomprising one or more computer readable media having computer readableprogram code embodied thereon.

Any combination of one or more computer readable media may be used. Thecomputer readable media may be a computer readable signal medium or acomputer readable storage medium. A computer readable storage medium maybe, for example, but not limited to, an electronic, magnetic, optical,electromagnetic, or semiconductor system, apparatus, or device, or anysuitable combination of the foregoing. More specific examples (anon-exhaustive list) of the computer readable storage medium wouldinclude the following: a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an appropriateoptical fiber with a repeater, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium may be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device. Program codeembodied on a computer readable signal medium may be transmitted usingany appropriate medium, including but not limited to wireless, wireline,optical fiber cable, radio frequency (RF), etc., or any suitablecombination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable instruction executionapparatus, create a mechanism for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that when executed can direct a computer, otherprogrammable data processing apparatus, or other devices to function ina particular manner, such that the instructions when stored in thecomputer readable medium produce an article of manufacture includinginstructions which when executed, cause a computer to implement thefunction/act specified in the flowchart and/or block diagram block orblocks. The computer program instructions may also be loaded onto acomputer, other programmable instruction execution apparatus, or otherdevices to cause a series of operational steps to be performed on thecomputer, other programmable apparatuses or other devices to produce acomputer implemented process such that the instructions which execute onthe computer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

It is to be understood that the terminology used herein is for thepurpose of describing particular embodiments only and is not intended tobe limiting of the invention. Unless otherwise defined, all terms(including technical and scientific terms) used herein have the samemeaning as commonly understood by one of ordinary skill in the art towhich this disclosure belongs. It will be further understood that terms,such as those defined in commonly used dictionaries, should beinterpreted as having a meaning that is consistent with their meaning inthe context of this specification and the relevant art and will not beinterpreted in an idealized or overly formal sense expressly so definedherein.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousaspects of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularaspects only and is not intended to be limiting of the disclosure. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items. Like reference numbers signify like elements throughoutthe description of the figures.

The corresponding structures, materials, acts, and equivalents of anymeans or step plus function elements in the claims below are intended toinclude any disclosed structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present disclosure has been presentedfor purposes of illustration and description, but is not intended to beexhaustive or limited to the disclosure in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of thedisclosure. The aspects of the disclosure herein were chosen anddescribed in order to best explain the principles of the disclosure andthe practical application, and to enable others of ordinary skill in theart to understand the disclosure with various modifications as aresuited to the particular use contemplated.

The invention claimed is:
 1. A resource management node comprising: aprocessor; and a memory coupled to the processor and comprising computerreadable program code that when executed by the processor causes theprocessor to perform operations comprising: for a plurality of guestvirtual machines (VMs), determining operational resources of physicalhost machines available in a distributed computing system that areneeded to provide the plurality of guest VMs by operations comprising:measuring electrical power consumption by the physical host machineswhile the physical host machines are processing the plurality of guestVMs; and for each of the plurality of guest VMs, proportionallyattributing the electrical power consumption, which is measured, to theguest VM based on the relative amount of processor resource of thephysical host machines that is utilized to process the guest VM;recursively determining an amount of electrical power capacity ofcabinets used by each of a plurality of different placements of theplurality of guest VMs on the physical host machines in the cabinets ofthe distributed computing system using the measured electrical powerconsumption that is proportionally attributed to each of the pluralityof guest VMs, to identify one of the plurality of different placementsthat satisfies a defined rule for how much electrical power capacity ofthe cabinets is allowed to be used and how much physical rack storagespace of the cabinets is allowed to be used, and that does not result inplacement in a same cabinet of a pair of the guest VMs that have beenidentified as not allowed to be hosted on physical host machines locatedin a same cabinet; recursively determining an amount of physical rackstorage space of the cabinets used by each of the plurality of differentplacements of the guest VMs on the plurality of physical host machinesin the cabinets of the distributed computing system to further identifythe one of the plurality of different placements that also satisfies thedefined rule for how much physical rack storage space of the cabinets isallowed to be used, and selecting the one of the plurality of differentplacements further responsive to determining that it distributes thephysical host machines among the cabinets to distribute amounts ofunused physical rack storage space between the cabinets according to thedefined rule for how much physical rack storage space; and controllingplacement of the plurality of guest VMs on the physical host machines inthe cabinets according to the one of the plurality of differentplacements that is identified.
 2. The resource management node of claim1, wherein recursively determining an amount of electrical powercapacity of cabinets used by each of a plurality of different placementsof the plurality of guest VMs on the physical host machines in thecabinets of the distributed computing system using the measuredelectrical power consumption that is proportionally attributed to eachof the plurality of guest VMs, to identify one of the plurality ofdifferent placements that satisfies a defined rule for how muchelectrical power capacity of the cabinets is allowed to be used,comprises: selecting the one of the plurality of different placementsresponsive to determining that it distributes loading on the electricalpower capacity of the cabinets according to a defined electrical powercapacity loading rule.
 3. The resource management node of claim 1,wherein proportionally attributing the electrical power consumption,which is measured, to the guest VM based on the relative amount ofprocessor resource of the physical host machines that is utilized toprocess the guest VM, comprises: identifying a group of the guest VMsthat perform overhead system services; and excluding the group of theguest VMs from the operation for proportional attributing the electricalpower consumption to the guest VMs.
 4. The resource management node ofclaim 3, wherein the identifying a group of the guest VMs that performoverhead system services, comprises: selecting a guest VM for inclusionin the group of the guest VMs that perform overhead system servicesbased on identifying the guest VM as performing a data archivingapplication, a virus scanning application, or a physical device managerapplication.
 5. A resource management node comprising: a processor; anda memory coupled to the processor and comprising computer readableprogram code that when executed by the processor causes the processor toperform operations comprising: for a plurality of guest virtual machines(VMs), determining operational resources of physical host machinesavailable in a distributed computing system that are needed to providethe plurality of guest VMs by operations comprising: measuring coolingconsumption by the physical host machines while the physical hostmachines are processing the plurality of guest VMs; and for each of theplurality of guest VMs, proportionally attributing the coolingconsumption, which is measured, to the guest VM based on the relativeamount of processor resource of the physical host machines that isutilized to process the guest VM; recursively determining an amount ofcooling capacity of cabinets used by each of a plurality of differentplacements of the plurality of guest VMs on the physical host machinesin the cabinets of the distributed computing system using the measuredcooling consumption that is proportionally attributed to each of theplurality of guest VMs, to identify one of the plurality of differentplacements that satisfies a defined rule for how much cooling capacityof the cabinets is allowed to be used and how much physical rack storagespace of the cabinets is allowed to be used, and that does not result inplacement in a same cabinet of a pair of the guest VMs that have beenidentified as not allowed to be hosted on physical host machines locatedin a same cabinet; recursively determining an amount of physical rackstorage space of the cabinets used by each of the plurality of differentplacements of the guest VMs on the plurality of physical host machinesin the cabinets of the distributed computing system to further identifythe one of the plurality of different placements that also satisfies thedefined rule for how much physical rack storage space of the cabinets isallowed to be used, and selecting the one of the plurality of differentplacements further responsive to determining that it distributes thephysical host machines among the cabinets to distribute amounts ofunused physical rack storage space between the cabinets according to thedefined rule for how much physical rack storage space; and controllingplacement of the plurality of guest VMs on the physical host machines inthe cabinets according to the one of the plurality of differentplacements that is identified.
 6. The resource management node of claim5, wherein recursively determining an amount of cooling capacity ofcabinets used by each of a plurality of different placements of theplurality of guest VMs on the physical host machines in the cabinets ofthe distributed computing system using the measured cooling consumptionthat is proportionally attributed to each of the plurality of guest VMs,to identify one of the plurality of different placements that satisfiesa defined rule for how much cooling capacity of the cabinets is allowedto be used, comprises: selecting the one of the plurality of differentplacements responsive to determining that it distributes loading on thecooling capacity of the cabinets according to a defined cooling capacityloading rule.
 7. The resource management node of claim 5, whereinproportionally attributing the cooling consumption, which is measured,to the guest VM based on the relative amount of processor resource ofthe physical host machines that is utilized to process the guest VM,comprises: identifying a group of the guest VMs that perform overheadsystem services; and excluding the group of the guest VMs from theoperation for proportional attributing the cooling consumption to theguest VMs.
 8. The resource management node of Claim 7, wherein theidentifying a group of the guest VMs that perform overhead systemservices, comprises: selecting a guest VM for inclusion in the group ofthe guest VMs that perform overhead system services based on identifyingthe guest VM as performing a data archiving application, a virusscanning application, or a physical device manager application.
 9. Amethod of operating a resource management node comprising: for aplurality of guest virtual machines (VMs), determining operationalresources of physical host machines available in a distributed computingsystem that are needed to provide the plurality of guest VMs byoperations comprising: measuring electrical power consumption by thephysical host machines while the physical host machines are processingthe plurality of guest VMs; and for each of the plurality of guest VMs,proportionally attributing the electrical power consumption, which ismeasured, to the guest VM based on the relative amount of processorresource of the physical host machines that is utilized to process theguest VM; recursively determining an amount of electrical power capacityof cabinets used by each of a plurality of different placements of theplurality of guest VMs on the physical host machines in the cabinets ofthe distributed computing system using the measured electrical powerconsumption that is proportionally attributed to each of the pluralityof guest VMs, to identify one of the plurality of different placementsthat satisfies a defined rule for how much electrical power capacity ofthe cabinets is allowed to be used and how much physical rack storagespace of the cabinets is allowed to be used, and that does not result inplacement in a same cabinet of a pair of the guest VMs that have beenidentified as not allowed to be hosted on physical host machines locatedin a same cabinet; recursively determining an amount of physical rackstorage space of the cabinets used by each of the plurality of differentplacements of the guest VMs on the plurality of physical host machinesin the cabinets of the distributed computing system to further identifythe one of the plurality of different placements that also satisfies thedefined rule for how much physical rack storage space of the cabinets isallowed to be used, and selecting the one of the plurality of differentplacements further responsive to determining that it distributes thephysical host machines among the cabinets to distribute amounts ofunused physical rack storage space between the cabinets according to thedefined rule for how much physical rack storage space; and controllingplacement of the plurality of guest VMs on the physical host machines inthe cabinets according to the one of the plurality of differentplacements that is identified.
 10. The method of claim 9, whereinrecursively determining an amount of electrical power capacity ofcabinets used by each of a plurality of different placements of theplurality of guest VMs on the physical host machines in the cabinets ofthe distributed computing system using the measured electrical powerconsumption that is proportionally attributed to each of the pluralityof guest VMs, to identify one of the plurality of different placementsthat satisfies a defined rule for how much electrical power capacity ofthe cabinets is allowed to be used, comprises: selecting the one of theplurality of different placements responsive to determining that itdistributes loading on the electrical power capacity of the cabinetsaccording to a defined electrical power capacity loading rule.
 11. Themethod of claim 9, further comprising: measuring cooling consumption bythe physical host machines while the physical host machines areprocessing the plurality of guest VMs; for each of the plurality ofguest VMs, proportionally attributing the cooling consumption, which ismeasured, to the guest VM based on the relative amount of processorresource of the physical host machines that is utilized to process theguest VM; and recursively determining an amount of cooling capacity ofthe cabinets used by each of the plurality of different placements ofthe plurality of guest VMs on the physical host machines in the cabinetsof the distributed computing system to further identify the one of theplurality of different placements that also satisfies a defined rule forhow much cooling capacity of the cabinets is allowed to be used.
 12. Themethod of claim 11, wherein recursively determining an amount of coolingcapacity of the cabinets used by each of the plurality of differentplacements of the plurality of guest VMs on the physical host machinesin the cabinets of the distributed computing system to further identifythe one of the plurality of different placements that also satisfies adefined rule for how much cooling capacity of the cabinets is allowed tobe used, comprises: selecting the one of the plurality of differentplacements that distributes loading on the cooling capacity of thecabinets according to a defined cooling capacity loading rule.
 13. Themethod of claim 9, wherein proportionally attributing the coolingconsumption, which is measured, to the guest VM based on the relativeamount of processor resource of the physical host machines that isutilized to process the guest VM, comprises: identifying a group of theguest VMs that perform overhead system services; and excluding the groupof the guest VMs from the operation for proportional attributing thecooling consumption to the guest VMs.
 14. The method of claim 13,wherein the identifying a group of the guest VMs that perform overheadsystem services, comprises: selecting a guest VM for inclusion in thegroup of the guest VMs that perform overhead system services based onidentifying the guest VM as performing a data archiving application, avirus scanning application, or a physical device manager application.